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Efficiently Enumerating Hitting Sets of Hypergraphs Arising in Data Profiling

机译:有效地枚举数据分析中产生的超照片集

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We devise an enumeration method for inclusion-wise minimal hitting sets in hypergraphs. It has delay O(m~k*~( +1) · n~2) and uses linear space. Hereby, n is the number of vertices, m the number of hyperedges, and k* the rank of the transversal hypergraph. In particular, on classes of hypergraphs for which the cardinality k* of the largest minimal hitting set is bounded, the delay is polynomial. The algorithm solves the extension problem for minimal hitting sets as a subroutine. We show that the extension problem is W-complete when parameterised by the cardinality of the set which is to be extended. For the subroutine, we give an algorithm that is optimal under the exponential time hypothesis. Despite these lower bounds, we provide empirical evidence showing that the enumeration outperforms the theoretical worst-case guarantee on hypergraphs arising in the profiling of relational databases, namely, in the detection of unique column combinations.
机译:我们设计了一种枚举方法,以便在超图中包含最小的击中集合。它具有延迟O(m〜k *〜(+1)·n〜2)并使用线性空间。因此,N是顶点的数量,H个超高频的数量,以及横向超图的等级。特别是,在最小最小击球设定集的基数K *界定的超微图类上,延迟是多项式。该算法解决了最小击中集作为子程序的扩展问题。我们表明,当要扩展的集合的基数,延伸问题是W-Treate。对于子程序,我们提供了一种在指数时间假设下最佳的算法。尽管有这些下限,但我们提供了实证证据,表明枚举优势了在关系数据库分析中出现的理论最坏情况保证,即在检测到独特的柱组合中。

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